6131271 1 Jan van Rijn 9954 Supervised Classification 7096 sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(1) 4163297 bootstrap true 6902 class_weight null 6902 criterion "entropy" 6902 max_depth null 6902 max_features "auto" 6902 max_leaf_nodes null 6902 min_impurity_split 1e-07 6902 min_samples_leaf 9 6902 min_samples_split 12 6902 min_weight_fraction_leaf 0.0 6902 n_estimators 10 6902 n_jobs 1 6902 oob_score false 6902 random_state 1 6902 verbose 0 6902 warm_start false 6902 axis 0 6947 categorical_features [] 6947 copy true 6947 fill_empty 0 6947 missing_values "NaN" 6947 strategy "median" 6947 strategy_nominal "most_frequent" 6947 verbose 0 6947 categorical_features [] 6948 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 6948 handle_unknown "ignore" 6948 n_values "auto" 6948 sparse true 6948 threshold 0.0 6949 cv null 7096 error_score "raise" 7096 fit_params {} 7096 iid true 7096 n_iter 50 7096 n_jobs 1 7096 param_distributions {"classifier__max_features": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], "classifier__bootstrap": [true, false], "classifier__min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__criterion": ["gini", "entropy"], "imputation__strategy": ["mean", "median", "most_frequent"]} 7096 pre_dispatch "2*n_jobs" 7096 random_state 1 7096 refit true 7096 return_train_score true 7096 scoring null 7096 verbose 0 7096 openml-python Sklearn_0.18.1. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 13193286 description https://api.openml.org/data/download/13193286/description.xml -1 13193287 predictions https://api.openml.org/data/download/13193287/predictions.arff -1 13193288 trace https://api.openml.org/data/download/13193288/trace.arff area_under_roc_curve 0.9899674479166669 [0.949574,0.998363,0.999605,0.999961,0.998895,0.990017,0.997593,0.963088,0.995778,0.99854,0.995699,0.998224,0.996528,0.981909,0.99925,0.977588,0.999566,0.985361,0.981435,0.959695,0.99712,0.991655,0.981751,1,0.994121,0.934521,0.980015,0.995778,0.991221,0.999092,0.999961,1,0.995699,0.990037,0.998461,0.99858,0.991951,0.982974,0.998501,0.999527,0.996291,0.997751,0.999645,0.99562,0.996291,0.989879,0.998106,0.993726,0.995462,0.993351,0.990649,0.982461,0.9811,0.982008,0.99349,0.980646,1,0.999842,0.991675,0.984967,0.999684,0.996725,0.959852,0.979384,0.998106,0.992326,0.974254,1,0.986348,0.985322,0.98548,0.997554,0.982106,0.988676,0.968119,0.989978,0.998698,0.961628,0.999842,0.999014,0.985894,0.993884,0.998146,0.99345,0.982678,0.995739,0.999842,0.986308,0.996607,0.998422,0.995975,0.999171,0.992188,0.985006,0.996212,0.999527,0.990294,0.989583,0.950797,0.993332] average_cost 0 f_measure 0.6816827331100107 [0.384615,0.857143,0.909091,0.967742,0.833333,0.375,0.903226,0.777778,0.580645,0.933333,0.764706,0.875,0.787879,0.5,0.941176,0.787879,0.833333,0.4375,0.545455,0.307692,0.810811,0.933333,0.642857,1,0.619048,0.153846,0.375,0.722222,0.903226,0.9375,0.909091,0.967742,0.882353,0.5,0.717949,0.717949,0.242424,0.235294,0.882353,0.882353,0.827586,0.764706,0.864865,0.611111,0.764706,0.666667,0.83871,0.827586,0.75,0.777778,0.606061,0.642857,0.709677,0.4,0.666667,0.296296,0.941176,0.842105,0.451613,0.538462,0.909091,0.756757,0.5625,0.545455,0.681818,0.606061,0.322581,0.914286,0.26087,0.466667,0.709677,0.705882,0.352941,0.8,0.363636,0.709677,0.888889,0.105263,0.914286,0.903226,0.434783,0.75,0.774194,0.545455,0.578947,0.875,0.967742,0.5,0.727273,0.9375,0.705882,0.833333,0.666667,0.461538,0.727273,0.833333,0.722222,0.592593,0.347826,0.580645] kappa 0.6919191919191918 kb_relative_information_score 1094.593270607665 mean_absolute_error 0.014027697957893976 mean_prior_absolute_error 0.019800000000000036 number_of_instances 1600 [16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16] precision 0.6897121729431932 [0.5,0.789474,0.882353,1,0.75,0.375,0.933333,0.7,0.6,1,0.722222,0.875,0.764706,0.583333,0.888889,0.764706,0.75,0.4375,0.529412,0.4,0.714286,1,0.75,1,0.5,0.2,0.375,0.65,0.933333,0.9375,0.882353,1,0.833333,0.5,0.608696,0.608696,0.235294,0.222222,0.833333,0.833333,0.923077,0.722222,0.761905,0.55,0.722222,0.714286,0.866667,0.923077,0.75,0.7,0.588235,0.75,0.733333,0.555556,0.818182,0.363636,0.888889,0.727273,0.466667,0.7,0.882353,0.666667,0.5625,0.529412,0.535714,0.588235,0.333333,0.842105,0.428571,0.5,0.733333,0.666667,0.333333,0.736842,0.666667,0.733333,0.8,0.333333,0.842105,0.933333,0.714286,0.75,0.8,0.529412,0.5,0.875,1,0.583333,0.705882,0.9375,0.666667,0.75,0.818182,0.6,0.705882,0.75,0.65,0.727273,0.571429,0.6] predictive_accuracy 0.695 prior_entropy 6.6438561897747395 recall 0.695 [0.3125,0.9375,0.9375,0.9375,0.9375,0.375,0.875,0.875,0.5625,0.875,0.8125,0.875,0.8125,0.4375,1,0.8125,0.9375,0.4375,0.5625,0.25,0.9375,0.875,0.5625,1,0.8125,0.125,0.375,0.8125,0.875,0.9375,0.9375,0.9375,0.9375,0.5,0.875,0.875,0.25,0.25,0.9375,0.9375,0.75,0.8125,1,0.6875,0.8125,0.625,0.8125,0.75,0.75,0.875,0.625,0.5625,0.6875,0.3125,0.5625,0.25,1,1,0.4375,0.4375,0.9375,0.875,0.5625,0.5625,0.9375,0.625,0.3125,1,0.1875,0.4375,0.6875,0.75,0.375,0.875,0.25,0.6875,1,0.0625,1,0.875,0.3125,0.75,0.75,0.5625,0.6875,0.875,0.9375,0.4375,0.75,0.9375,0.75,0.9375,0.5625,0.375,0.75,0.9375,0.8125,0.5,0.25,0.5625] relative_absolute_error 0.7084695938330278 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.07689637827813983 root_relative_squared_error 0.7728376802600753 total_cost 0 area_under_roc_curve 0.9916035994347588 [0.955975,1,1,1,0.993671,1,1,1,0.993671,1,0.981013,1,0.996835,0.993671,1,1,1,0.990506,1,0.987421,1,1,0.993711,1,0.993711,0.987342,0.955696,0.996835,0.977848,1,1,1,1,1,1,0.993711,0.993711,0.993711,0.993711,1,1,0.993711,0.996835,0.996835,1,0.974684,1,1,1,0.973101,0.990506,1,1,1,1,1,1,1,0.968553,1,1,1,0.996835,0.993671,1,1,0.96519,1,1,0.91195,0.993671,1,0.987342,1,1,1,1,0.974684,1,1,0.987342,1,0.987421,1,0.968354,0.981013,1,1,0.996835,1,0.990506,1,1,0.971519,1,0.996835,0.993711,0.996835,0.848101,1] area_under_roc_curve 0.9936390414775895 [0.943396,1,1,1,1,1,0.993671,1,1,1,0.993671,1,0.996835,0.952532,1,1,1,0.993671,0.981013,0.993711,1,0.937107,1,1,1,0.946203,0.996835,1,1,1,1,1,1,0.993671,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.996835,0.96519,1,0.984177,1,0.955975,1,1,1,0.977848,1,1,1,1,1,0.993671,0.993671,1,1,0.987421,0.993671,1,0.990506,1,1,1,1,0.990506,1,0.984177,0.96519,0.996835,1,1,1,1,1,0.990506,1,1,0.987342,1,0.996835,0.984177,0.996835,1,0.993711,0.981013,0.974684,1] area_under_roc_curve 0.9914285785367406 [1,1,1,1,1,0.984177,1,1,0.987342,1,1,1,1,0.968354,0.993671,0.977848,1,0.977848,0.974843,0.920886,1,1,0.990506,1,1,0.987342,0.996835,0.987421,1,1,1,1,0.977848,0.977848,1,1,0.993711,0.949686,1,1,0.996835,1,1,1,0.993671,0.981132,0.987342,1,0.981013,1,0.977848,0.993711,1,0.993671,1,0.981013,1,1,0.943396,0.984177,1,0.993711,1,0.987421,1,0.990506,1,1,0.993711,0.984177,1,1,0.974843,1,0.993711,1,1,0.933544,1,1,0.981132,1,0.996835,1,1,1,1,1,0.993671,1,1,1,1,0.949367,1,1,0.996835,0.990506,0.937107,1] area_under_roc_curve 0.990206044502826 [0.990506,0.988924,0.993671,1,0.996835,0.990506,1,1,0.996835,1,0.996835,1,1,0.996835,1,1,1,1,0.830189,0.920886,1,1,1,1,0.949686,0.952532,0.962025,1,1,1,1,1,1,0.990506,1,1,1,0.981132,1,1,0.996835,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,0.968354,1,0.990506,1,1,1,0.977848,1,1,0.996835,1,0.996835,0.990506,0.949367,1,1,0.996835,0.966772,1,0.943396,0.993671,1,0.987342,1,0.911392,1,1,0.949686,0.996835,0.993671,0.981013,1,1,1,1,1,1,1,1,0.955975,1,1,1,0.987342,0.993671,0.981132,0.974684] area_under_roc_curve 0.9897506418676855 [1,1,1,1,1,1,0.987421,0.996835,1,1,1,0.993671,1,0.893082,1,0.93038,1,0.990506,1,0.987342,0.984177,1,0.993671,1,0.996835,0.987342,0.968354,1,1,0.990506,1,1,1,0.976266,1,1,1,0.981013,1,1,0.993671,0.996835,1,1,1,1,1,0.996835,1,1,0.993711,0.968553,0.898734,0.981132,0.943038,0.996835,1,0.993671,0.993671,0.962264,1,1,0.993711,1,1,1,0.943396,1,0.974684,0.987342,1,1,0.981132,1,0.924051,1,0.993671,0.968354,1,1,1,1,1,0.987342,1,0.974843,1,0.993711,1,1,1,0.996835,1,0.987421,0.974684,1,0.996835,1,0.943396,0.984177] area_under_roc_curve 0.9864206422657429 [0.678797,1,1,1,0.993711,0.984177,1,1,1,0.993671,0.996835,1,0.987421,1,1,1,1,1,0.993711,0.968354,1,0.96519,0.870253,1,0.993671,0.977848,0.981013,1,1,1,1,1,0.990506,0.996835,1,0.996835,0.987342,0.968354,1,1,1,0.996835,1,1,1,1,1,0.977848,0.996835,0.981013,0.981132,0.993711,1,1,0.993671,0.984177,1,1,0.993671,1,1,0.996835,0.987421,1,0.993671,1,0.987421,1,0.968354,1,0.949367,0.990506,0.993711,1,0.892405,1,0.996835,0.987342,1,1,0.993711,0.993671,1,0.990506,0.974843,1,1,0.981132,0.987342,1,1,1,0.993711,1,1,1,1,0.968553,0.955975,1] area_under_roc_curve 0.9898637150704561 [0.993671,1,1,1,1,1,1,1,0.993711,1,1,1,0.987342,1,1,0.990506,1,0.993711,0.996835,0.946203,1,1,0.993671,1,0.993671,0.408805,0.987421,0.974684,1,1,1,1,1,1,1,1,0.996835,0.990506,1,0.993711,1,1,1,1,0.981132,1,1,0.984177,1,1,0.987421,0.984177,1,1,0.990506,0.946203,1,1,0.990506,0.968553,1,0.993671,1,0.920886,1,0.993711,0.987421,1,0.993671,0.990506,0.993711,1,1,1,0.996835,1,1,0.974843,1,1,0.990506,1,0.993671,0.993671,0.996835,1,1,0.981132,1,1,1,1,0.981013,1,1,1,1,1,0.955696,1] area_under_roc_curve 0.9892454820476081 [1,1,1,1,1,0.981013,0.996835,1,1,1,0.993711,1,0.996835,1,1,0.939873,1,0.949686,0.984177,0.927215,0.996835,1,1,1,1,0.981132,0.981132,1,0.899371,1,1,1,1,1,0.996835,1,0.962025,0.981013,1,1,1,1,1,0.996835,0.987421,1,1,1,0.993711,1,0.993711,1,0.949367,1,1,0.993671,1,1,0.993671,1,1,0.987342,1,0.96519,1,0.949686,0.918239,1,0.981013,0.974684,1,1,0.990506,0.915094,0.996835,1,1,0.91195,1,1,0.996835,0.993711,0.996835,0.996835,0.93038,0.996835,1,0.943396,1,1,1,1,1,0.974843,0.993711,1,0.946203,0.993711,0.996835,1] area_under_roc_curve 0.9928882055568824 [0.981132,0.993671,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.981132,1,1,1,1,0.974843,1,1,0.962264,0.993711,0.996835,0.996835,1,1,1,1,1,1,1,0.996835,0.971519,1,1,0.987421,0.990506,1,0.971519,0.993671,0.936709,1,1,0.993711,1,1,0.987342,1,0.946203,1,0.974843,1,1,0.996835,0.984177,1,1,0.996835,0.993671,1,1,1,1,0.984177,0.993711,1,0.987421,0.987342,1,0.962025,0.949367,1,1,1,1,0.987342,1,1,0.981132,0.993671,1,1,0.996835,0.987421,1,1,0.996835,0.996835,0.981013,0.993711,1,0.993711,1,0.952532,1] area_under_roc_curve 0.9880647440490403 [1,1,1,1,1,1,1,0.424528,0.993671,1,0.987421,1,1,0.993671,0.996835,1,1,0.968553,0.993671,0.987421,1,1,1,1,0.993671,0.974843,1,1,1,1,1,1,1,0.981132,1,1,0.996835,0.996835,0.996835,1,1,0.996835,1,0.993671,0.996835,0.992089,1,0.996835,1,1,1,0.939873,1,0.984177,1,1,1,1,0.993671,1,1,1,0.721519,0.981013,0.987421,0.993711,0.976266,1,1,0.993711,1,1,0.977848,1,1,1,0.993711,1,0.993711,1,1,0.949686,1,0.987421,1,1,0.996835,0.936709,1,0.993671,0.993671,1,1,1,1,1,1,1,1,1] average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 kappa 0.7347124077217639 kappa 0.6905344596194837 kappa 0.715752072641137 kappa 0.6968141802534443 kappa 0.66525875340465 kappa 0.658875552747947 kappa 0.7030484915495183 kappa 0.6652323240298448 kappa 0.684156500454025 kappa 0.7031305514981644 kb_relative_information_score 107.4790369606146 kb_relative_information_score 108.03855753432389 kb_relative_information_score 106.53640231213863 kb_relative_information_score 110.33203741179508 kb_relative_information_score 105.84859830240286 kb_relative_information_score 109.01352295717896 kb_relative_information_score 112.75176075724441 kb_relative_information_score 109.03929443622437 kb_relative_information_score 113.90178808524453 kb_relative_information_score 111.65227185049673 mean_absolute_error 0.014414874142401593 mean_absolute_error 0.014365576603747283 mean_absolute_error 0.014528431968304098 mean_absolute_error 0.013860869418571628 mean_absolute_error 0.014605309909818332 mean_absolute_error 0.01399238515875453 mean_absolute_error 0.013512044791400887 mean_absolute_error 0.01391684386487605 mean_absolute_error 0.013306371947088258 mean_absolute_error 0.013774271773977085 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 number_of_instances 160 [1,2,2,1,2,1,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,1,2,2,2,2,2,1,1,1,1,2,1,1,2,2,2,2,1,1,2,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,1,2,2,1,1,1,2,1,2,2,1,2,2,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,1,2,2,1] number_of_instances 160 [1,2,2,1,2,1,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,1,2,2,2,2,2,1,1,1,1,2,1,1,2,2,2,2,1,1,2,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,1,2,2,1,1,1,2,1,2,2,1,2,2,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,1,2,2,1] number_of_instances 160 [2,2,2,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,1,1,2,1,1,2,2,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,2,1,2,1,2,2,2,1,1,2,1,2,1,2,1,2,1,1,2,1,2,2,2,2,1,2,2,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,1,1,2,2,1,2,2,1,2] number_of_instances 160 [2,2,2,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,1,1,2,1,1,2,2,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,2,1,2,1,2,2,2,1,1,2,1,2,1,2,1,2,1,1,2,1,2,2,2,2,1,2,2,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,1,1,2,2,1,2,2,1,2] number_of_instances 160 [2,1,2,2,1,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,2,1,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,1,1,1,2,2,2,2,1,1,2,1,2,2,1,2,2,1,2,2,1,1,2,2,1,2,2,2,2,2,1,1,2,1,2,2,2,1,1,2,2,2,1,1,1,1,2,2,1,2,1,1,2,1,2,1,1,2] number_of_instances 160 [2,1,2,2,1,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,2,1,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,1,1,1,2,2,2,2,1,1,2,1,2,2,1,2,2,1,2,2,1,1,2,2,1,2,2,2,2,2,1,1,2,1,2,2,2,1,1,2,2,2,1,1,1,1,2,2,1,2,1,1,2,1,2,1,1,2] number_of_instances 160 [2,1,1,2,1,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,2,1,1,2,1,2,2,1,1,1,2,2,2,2,2,1,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,2,1,1,1,1,1,2,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,1,2,2] number_of_instances 160 [2,1,1,2,1,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,2,1,1,2,1,2,2,1,1,1,2,2,2,2,2,1,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,2,1,1,1,1,1,2,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,1,2,2] number_of_instances 160 [1,2,1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,2,1,2,2,1,1,2,2,1,2,2,1,1,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,2,2,1,1,2,1,2,1,1,1,2,2,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,2,2,2,2,1,2,1,2,2,1] number_of_instances 160 [1,2,1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,2,1,2,2,1,1,2,2,1,2,2,1,1,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,2,2,1,1,2,1,2,1,1,1,2,2,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,2,2,2,2,1,2,1,2,2,1] predictive_accuracy 0.7375 predictive_accuracy 0.69375 predictive_accuracy 0.71875 predictive_accuracy 0.7 predictive_accuracy 0.66875 predictive_accuracy 0.6625 predictive_accuracy 0.70625 predictive_accuracy 0.66875 predictive_accuracy 0.6875 predictive_accuracy 0.70625 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 recall 0.7375 [0,1,1,1,0.5,1,1,1,0,1,0.5,1,1,0.5,1,1,1,0,1,0,1,1,0,1,0,0.5,0,1,0.5,1,1,1,1,0,0,1,0,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,0.5,0.5,1,0.5,0.5,1,0,0,1,1,0.5,1,1,1,1,0,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0,1] recall 0.69375 [0,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1,1,0.5,0.5,0.5,1,1,0,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,0,1,0,1,1,1,0,1,1,1,0.5,1,0.5,0,1,0,0,1,1,0.5,1,0,0,1,0,1,0,0.5,1,1,1,1,1,1,0.5,0.5,1,0.5,1,0.5,0.5,0.5,1,1,0,0,0] recall 0.71875 [0.5,0.5,1,1,1,0,1,1,0.5,1,1,0.5,1,0.5,1,0.5,1,0.5,0,0,1,1,0.5,1,1,0,1,0,1,1,1,1,0.5,0.5,1,1,0,0,1,1,0.5,1,1,1,1,0,0.5,1,0.5,1,0.5,1,0,0.5,1,0,1,1,0,0.5,1,1,0.5,1,1,1,0.5,1,0,1,1,1,0,0.5,0,1,1,0,1,1,0,1,1,0.5,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0,1] recall 0.7 [0,1,0.5,1,1,0,1,1,0.5,0.5,1,1,1,0.5,1,1,1,0.5,0,0,1,1,0.5,1,0,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,0.5,1,1,1,0,1,1,1,1,1,0.5,0,1,0,0.5,0,0.5,0,1,1,0.5,1,0,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,1,0,0,0.5,1,0.5,0.5,0,0.5] recall 0.66875 [1,1,1,1,1,0.5,0,1,0,1,1,0.5,1,0,1,0.5,1,0.5,1,1,0.5,1,0.5,1,0.5,0,0,1,1,0.5,1,0,1,0.5,1,1,0.5,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0,0,0.5,0,0,0.5,1,1,0.5,0,1,1,1,1,0.5,0.5,0,1,0,0.5,1,1,0,1,0,1,1,0,1,1,1,1,1,0,1,1,1,0,1,1,1,0.5,1,0,0.5,1,0.5,1,0,0] recall 0.6625 [0,1,1,0.5,1,0.5,1,1,0,0.5,0.5,1,0,1,1,1,1,1,0,0,1,0.5,0.5,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,0,1,1,0,0,0,1,1,0,1,1,1,0,0,1,1,1,1,0,0.5,0.5,0,0,1,0.5,1,1,0,1,1,0,0.5,1,0.5,0,1,1,0,0.5,1,1,1,0,1,0.5,1,1,0,0,0.5] recall 0.70625 [0.5,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,0.5,1,1,0,0,0.5,1,1,1,1,1,1,1,0.5,0,0.5,1,0,0.5,0.5,1,1,0,0.5,0.5,0.5,1,1,0,0.5,1,0,1,0,1,1,0,0,0.5,0.5,1,0,1,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0,1,1,0.5,1,0.5,1,1,1,1,0,0,1,1,1,0,0,1,0.5,1,0,0.5,1] recall 0.66875 [0,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,0,1,0.5,1,0,0.5,0,1,1,1,1,1,0,1,1,0,1,0.5,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,0.5,0.5,1,1,1,1,1,0.5,0,0.5,1,0,0,1,0,0,0,1,0.5,0,0,1,1,0,1,1,0,0,0,1,0,0.5,1,0,1,1,0,1,1,0,1,1,0.5,1,0,0.5] recall 0.6875 [0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,0,1,1,0,1,1,0,0,0.5,1,1,1,1,1,0,1,1,0.5,0,1,1,0,0.5,1,0.5,0.5,0,1,0.5,0,1,1,0,1,0,0.5,0,1,1,0.5,0.5,1,1,0.5,1,1,0,1,1,0.5,0,0,0,0,1,0,0,1,1,1,1,0,1,1,0,0.5,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,0.5,1] recall 0.70625 [1,1,1,1,1,0,1,0,1,1,0,1,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,0,0.5,0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,0.5,0.5,0,0.5,0,1,1,0.5,0.5,1,1,0,0.5,1,1,0,1,0.5,1,1,1,0.5,1,0,1,1,0,1,1,0.5,0,1,0,1,1,0.5,0,1,0.5,0.5,1,1,0.5,1,1,1,0,1,0] relative_absolute_error 0.7280239465859378 relative_absolute_error 0.7255341719064272 relative_absolute_error 0.7337591903183877 relative_absolute_error 0.7000439100288689 relative_absolute_error 0.7376419146372882 relative_absolute_error 0.7066861191290156 relative_absolute_error 0.6824265046162052 relative_absolute_error 0.702870902266466 relative_absolute_error 0.6720389872266785 relative_absolute_error 0.6956702916150032 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_squared_error 0.07805923487772606 root_mean_squared_error 0.07773050994243409 root_mean_squared_error 0.07870642969467044 root_mean_squared_error 0.07643585097302165 root_mean_squared_error 0.07887038506224886 root_mean_squared_error 0.0771510582523066 root_mean_squared_error 0.07515105235615725 root_mean_squared_error 0.07663755308223331 root_mean_squared_error 0.07411590703272239 root_mean_squared_error 0.07596792672448988 root_relative_squared_error 0.7845248288231543 root_relative_squared_error 0.7812210189152837 root_relative_squared_error 0.791029381471843 root_relative_squared_error 0.7682092066940436 root_relative_squared_error 0.7926771949161534 root_relative_squared_error 0.7753973103083439 root_relative_squared_error 0.755296494744629 root_relative_squared_error 0.7702363891657962 root_relative_squared_error 0.7448928928012214 root_relative_squared_error 0.7635063910495318 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 usercpu_time_millis 44418.09254300097 usercpu_time_millis 44478.21425499933 usercpu_time_millis 44336.530954999034 usercpu_time_millis 44793.38196699973 usercpu_time_millis 44344.641107003554 usercpu_time_millis 44409.81389600347 usercpu_time_millis 45482.90044400346 usercpu_time_millis 44776.35778200056 usercpu_time_millis 44366.8136319975 usercpu_time_millis 44516.532621000806 usercpu_time_millis_testing 4.040298001200426 usercpu_time_millis_testing 4.034353998576989 usercpu_time_millis_testing 4.128215998207452 usercpu_time_millis_testing 4.129708999244031 usercpu_time_millis_testing 4.050963001645869 usercpu_time_millis_testing 4.0022780012805015 usercpu_time_millis_testing 4.074653003044659 usercpu_time_millis_testing 4.032277000078466 usercpu_time_millis_testing 4.040651998366229 usercpu_time_millis_testing 4.045940000651171 usercpu_time_millis_training 44414.052244999766 usercpu_time_millis_training 44474.17990100075 usercpu_time_millis_training 44332.40273900083 usercpu_time_millis_training 44789.25225800049 usercpu_time_millis_training 44340.59014400191 usercpu_time_millis_training 44405.81161800219 usercpu_time_millis_training 45478.82579100042 usercpu_time_millis_training 44772.32550500048 usercpu_time_millis_training 44362.772979999136 usercpu_time_millis_training 44512.486681000155